Table 4.

Average classification accuracy of different models with multi-condition training.

SNR	MLP	CNN	RNN	LSTM	SOM-SNN
Clean	96.10 ± 1.18%	97.60 ± 0.89%	94.30 ± 3.04%	98.15 ± 0.71%	99.80 ± 0.22%
20 dB	98.45 ± 0.61%	99.50 ± 0.22%	94.30 ± 2.70%	99.10 ± 0.89%	100.00 ± 0.00%
10 dB	99.35 ± 0.45%	99.70 ± 0.33%	95.25 ± 2.49%	99.05 ± 1.25%	100.00 ± 0.00%
0 dB	98.20 ± 1.45%	99.45 ± 0.75%	93.65 ± 2.82%	95.80 ± 3.93%	99.45 ± 0.55%
-5 dB	92.50 ± 1.53%	98.35 ± 0.78%	86.85 ± 5.20%	91.35 ± 4.82%	98.70 ± 0.48%
Average	96.92%	98.92%	92.87%	96.69%	99.59%

Experiments are conducted over 10 runs with random weight initialization.

The bold values indicate the best classification accuracies under different SNR.